Biomedical subjects
Publications and source records attributed to S C Pratt.
A swarm of honeybees (Apis mellifera) is capable of selecting one nest-site when faced with a choice of several. We adapt classical mathematical models of disease, information and competing beliefs to such decision-making processes. We show that the collective decision may be arrived at without the necessity for any bee to make any comparison between sites.
Honey bees have long been assumed to build their comb with the cells in either of two preferred orientations with respect to gravity ("vertical" or "horizontal"). I show here that these typical cell orientations in fact derive from substrate orientation and a simple building rule, rather than the influence of gravity itself. When bees were induced to build comb on substrates at four different orientations with respect to gravity, they always made cells with one vertex pointing directly toward the substrate. This produced horizontal and vertical cells on vertical and horizontal substrates, respectively, but yielded intermediate orientations on oblique substrates. The apparent preference for vertical and horizontal cells may simply reflect substrate orientation in the rectilinear hives from which cell measurements have been taken.
Methods based on variance components are powerful tools for linkage analysis of quantitative traits, because they allow simultaneous consideration of all pedigree members. The central idea is to identify loci making a significant contribution to the population variance of a trait, by use of allele-sharing probabilities derived from genotyped marker loci. The technique is only as powerful as the methods used to infer these probabilities, but, to date, no implementation has made full use of the inheritance information in mapping data. Here we present a new implementation that uses an exact multipoint algorithm to extract the full probability distribution of allele sharing at every point in a mapped region. At each locus in the region, the program fits a model that partitions total phenotypic variance into components due to environmental factors, a major gene at the locus, and other unlinked genes. Numerical methods are used to derive maximum-likelihood estimates of the variance components, under the assumption of multivariate normality. A likelihood-ratio test is then applied to detect any significant effect of the hypothesized major gene. Simulations show the method to have greater power than does traditional sib-pair analysis. The method is freely available in a new release of the software package GENEHUNTER.
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